![]() ![]() CREDIT: Sid AssawaworraritĪt night, solar cells radiate and lose heat to the sky, reaching temperatures a few degrees below the ambient air. The device generates electricity at night from the temperature difference between the solar cell and its surroundings. The device makes use of the heat leaking from Earth back into space – energy that is on the same order of magnitude as incoming solar radiation. In Applied Physics Letters, by AIP Publishing, researchers from Stanford University constructed a photovoltaic cell that harvests energy from the environment during the day and night, avoiding the need for batteries altogether. Solar cells provide power during the day, but saving energy for later use requires substantial battery storage. WASHINGTON, Ap– About 750 million people in the world do not have access to electricity at night. "Machine learning looks at numbers and patterns, and evolutionary algorithms facilitate the simulations.Article: Nighttime electric power generation at a density of 50mW/m2 via radiative cooling of a photovoltaic cell That's what captures the fundamental physics," he said. ![]() "At the end of the day, molecular dynamics is the physical engine. Coupling these methods together, Balasubramanian's team was able to reduce the time required to reach an optimal process by 40 percent. They harnessed the data to train a class of machine learning algorithms known as support vector machines to identify parameters in the materials and production process that would generate the most energy conversion efficiency, while maintaining structural strength and stability. These included altering the proportion of donor and receptor molecules in the bulk heterojunctions, and the temperature and amount of time spent in annealing-a cooling and hardening process that contributes to the stability of the product. Writing in Computational Materials Science in February 2021, Balasubramanian and Munshi along with Wei Chen (Northwestern University), and TeYu Chien (University of Wyoming) described results from a set of virtual experiments on Frontera testing the effects of various design changes. There's not a lot of real physics involved in it." Machine learning per se is simply mathematics. That's where I think lies the most benefit. "But more and more, there's an interest in using physics-educated machine learning. "A lot of research uses machine learning on raw data," Balasubramanian said. Balasubramanian believes the combination helps prevent artificial intelligence from coming up with unrealistic solutions. Basically, we're trying to understand how structure changes correlate with the efficiency of the solar conversion?"īalasubramanian uses what he calls "physics-informed machine learning." His research combines coarse-grained simulation-using approximate molecular models that represent the organic materials-and machine learning. ![]() "We mimicked how these cells are created, in particular the bulk heterojunction-the absorption layer of a solar cell. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said. They described the computational effort and associated findings in the May issue of IEEE Computing in Science and Engineering. Using the Frontera supercomputer at the Texas Advanced Computing Center ( TACC)-one of the most powerful on the planet-Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient?īalasubramanian, an associate professor of mechanical engineering and mechanics, studies the basic physics of the materials at the heart of solar energy conversion-the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed-as well as the manufacturing processes that produce commercial solar cells. Organic photovoltaics max out at 15 to 20 percent efficiency-substantial, but a limit on solar energy's potential. One is the ability to more efficiently transform photons of light from the Sun into usable energy. To reach that point, and to make solar power more affordable, solar technologies still require a number of breakthroughs. However, by 2050, renewables are predicted to be the most used energy source (surpassing petroleum and other liquids, natural gas, and coal) and solar will overtake wind as the leading source of renewable power. Today, solar energy provides 2 percent of U.S. This story originally appeared on the Texas Advanced Computing Center (The University of Texas at Austin) website. ![]()
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